{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "import pickle\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "# needed for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "current_palette = sns.color_palette()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.0-rc1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Requires TensorFlow V.12\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getkey(item):\n",
    "    fpr, tpr, _ = roc_curve(test_labels.ravel(), test_preds[item])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    return roc_auc\n",
    "\n",
    "def plotROC(testlabels, test_preds):\n",
    "    classifiers = list(test_preds.keys())\n",
    "\n",
    "    # Plot all ROC curves\n",
    "    plt.figure(figsize=(15,9))\n",
    "    for i, clf in zip(range(len(classifiers)), sorted(classifiers, key=getkey, reverse=True)):\n",
    "        fpr, tpr, _ = roc_curve(testlabels.ravel(), test_preds[clf] )\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr,\n",
    "                 label='ROC curve '+ clf +  ' (area = {0:0.4f})'\n",
    "                       ''.format(roc_auc), linestyle='-', linewidth=2)\n",
    "\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "    plt.xlim([-0.1, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Comparison of multiclass ROC curves')\n",
    "    plt.legend(loc=\"lower right\", fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pickle.load(open('193_features.p', 'rb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['shape', 'label', 'fold', 'dog_bark', 'air_cond', 'jackhamm', 'siren',\n",
      "       'engine_i', 'street_m', 'car_horn', 'gun_shot', 'children', 'drilling',\n",
      "       'sample'],\n",
      "      dtype='object')\n",
      "(8730, 14)\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working dataframe's shape: (8730, 194)\n"
     ]
    }
   ],
   "source": [
    "s = list(data['sample'])\n",
    "s = pd.DataFrame(s)\n",
    "data_cols = s.columns\n",
    "s['label'] = data['label']\n",
    "print('working dataframe\\'s shape:', s.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:465: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape (6984, 194)\n",
      "test  shape (1746, 194)\n",
      "common factors: 1, 2, 3, 6, 9, 18, 97, 194, 291, 582, 873, 1746\n"
     ]
    }
   ],
   "source": [
    "test_preds = {}\n",
    "train = s[0:6984]\n",
    "test = s[6984:]\n",
    "LB = LabelBinarizer().fit(train['label'])\n",
    "test_labels = LB.transform(test['label'])\n",
    "scaler1 = sk.preprocessing.StandardScaler().fit(train.loc[:,data_cols])\n",
    "train.loc[:,data_cols] = scaler1.transform(train.loc[:,data_cols])\n",
    "test.loc[:,data_cols] = scaler1.transform(test.loc[:,data_cols])\n",
    "\n",
    "# truncate data\n",
    "#data_cols = data_cols[:1000]\n",
    "\n",
    "# print shapes\n",
    "print('train shape {}\\ntest  shape {}\\ncommon factors: 1, 2, 3, 6, 9, 18, 97, 194, 291, 582, 873, 1746'.format(train.shape, test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del data, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def accuracy(predictions, labels):\n",
    "    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
    "            / predictions.shape[0])\n",
    "\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.01)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def test_accuracy(session, test_data=test, during = True):\n",
    "    test_data.reset_index(inplace=True, drop=True)\n",
    "    epoch_pred = session.run(test_prediction, feed_dict={tf_data : test_data.loc[0:check_size-1,data_cols], keep_prob : 1.0})\n",
    "    for i in range(check_size, test_data.shape[0], check_size):\n",
    "        epoch_pred = np.concatenate([epoch_pred, session.run(test_prediction, \n",
    "                                    feed_dict={tf_data : test_data.loc[i:i+check_size-1,data_cols], keep_prob : 1.0})], axis=0)\n",
    "    if during:\n",
    "        return accuracy(epoch_pred, test_labels)\n",
    "    else:\n",
    "        return epoch_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Run Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_labels = 10\n",
    "batch_size = 97\n",
    "acc_over_time = {}\n",
    "def Run_Session(num_epochs, name, k_prob=1.0, mute=False, record=False):\n",
    "    global train\n",
    "    \n",
    "    start = timer()\n",
    "    with tf.Session(graph=graph) as session:\n",
    "        if record:\n",
    "            merged = tf.merge_all_summaries()  \n",
    "            writer = tf.train.SummaryWriter(\"/tmp/tensorflowlogs\", session.graph)\n",
    "        #tf.initialize_all_variables().run()\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        print(\"Initialized\")\n",
    "        accu = []\n",
    "        \n",
    "        for epoch in range(num_epochs):\n",
    "            \n",
    "            # get batch\n",
    "            train_batch = train.sample(batch_size)\n",
    "            \n",
    "            t_d = train_batch[data_cols]\n",
    "            t_l = LB.transform(train_batch['label'])\n",
    "            \n",
    "            # make feed dict\n",
    "            feed_dict = { tf_data : t_d, train_labels : t_l, keep_prob : k_prob}\n",
    "            \n",
    "            # run model on batch\n",
    "            _, l, predictions = session.run([optimizer, loss, prediction], feed_dict=feed_dict)\n",
    "            \n",
    "            # mid model accuracy checks \n",
    "            if (epoch % 1000 == 0) and not mute:\n",
    "                print(\"\\tMinibatch loss at epoch {}: {}\".format(epoch, l))\n",
    "                print(\"\\tMinibatch accuracy: {:.1f}\".format(accuracy(predictions, t_l)))\n",
    "            if (epoch % 1000 == 0) and not mute:\n",
    "                print(\"Test accuracy: {:.1f}\".format(test_accuracy(session, during=True)))\n",
    "            if (epoch % 1000 == 0) and not mute:\n",
    "                accu.append(tuple([epoch, test_accuracy(session, during=True)]))\n",
    "                \n",
    "        # record accuracy and predictions\n",
    "        test_preds[name] = test_accuracy(session, during=False)\n",
    "        print(\"Final Test accuracy: {:.1f}\".format(accuracy(test_preds[name], test_labels)))\n",
    "        end = timer()\n",
    "        test_preds[name] = test_preds[name].ravel()\n",
    "        acc_over_time[name] = accu\n",
    "        print(\"time taken: {0} minutes {1:.1f} seconds\".format((end - start)//60, (end - start)%60))\n",
    "        #tf.train.export_meta_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DeepNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic DeepNN model made\n"
     ]
    }
   ],
   "source": [
    "# constants\n",
    "num_labels = 10\n",
    "\n",
    "batch_size = 97\n",
    "check_size = 582\n",
    "feature_size = 193\n",
    "\n",
    "n_hidden1 = 800\n",
    "n_hidden2 = 1000\n",
    "n_hidden3 = 800\n",
    "\n",
    "beta = 0.01\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # placeholders\n",
    "    tf_data = tf.placeholder(tf.float32, shape=[None, feature_size])\n",
    "    train_labels = tf.placeholder(tf.float32, shape=[None, num_labels])\n",
    "    keep_prob = tf.placeholder(tf.float32)\n",
    "    \n",
    "    # weights and biases\n",
    "    layer1_weights = weight_variable([feature_size, n_hidden1])\n",
    "    layer1_biases = bias_variable([n_hidden1])\n",
    "    layer2_weights = weight_variable([n_hidden1, n_hidden2])\n",
    "    layer2_biases = bias_variable([n_hidden2])\n",
    "    layer3_weights = weight_variable([n_hidden2, n_hidden3])\n",
    "    layer3_biases = bias_variable([n_hidden3])\n",
    "    layer4_weights = weight_variable([n_hidden3, num_labels])\n",
    "    layer4_biases = bias_variable([num_labels])\n",
    "\n",
    "    # model\n",
    "    def model(data, proba=1.0):\n",
    "        layer1 = tf.nn.relu(tf.matmul(data, layer1_weights) + layer1_biases)\n",
    "        layer1 = tf.nn.dropout(layer1, proba)\n",
    "        \n",
    "        layer2 = tf.nn.relu(tf.matmul(layer1, layer2_weights) + layer2_biases)\n",
    "        layer2 = tf.nn.dropout(layer2, proba)\n",
    "        \n",
    "        layer3 = tf.nn.relu(tf.matmul(layer2, layer3_weights) + layer3_biases)\n",
    "        layer3 = tf.nn.dropout(layer3, proba)\n",
    "        \n",
    "        return tf.matmul(layer3, layer4_weights) + layer4_biases\n",
    "\n",
    "    # Training computation.\n",
    "    logits = model(tf_data, keep_prob)\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, train_labels) +\n",
    "                         beta*tf.nn.l2_loss(layer1_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer1_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer2_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer2_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer3_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer3_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer4_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer4_biases))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)\n",
    "\n",
    "    prediction = tf.nn.softmax(logits)\n",
    "    test_prediction = tf.nn.softmax(model(tf_data, proba=1.0))  \n",
    "    print('Basic DeepNN model made')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "\tMinibatch loss at epoch 0: 3.115072727203369\n",
      "\tMinibatch accuracy: 12.4\n",
      "Test accuracy: 9.7\n",
      "\tMinibatch loss at epoch 1000: 1.2293397188186646\n",
      "\tMinibatch accuracy: 71.1\n",
      "Test accuracy: 57.4\n",
      "\tMinibatch loss at epoch 2000: 0.8796199560165405\n",
      "\tMinibatch accuracy: 83.5\n",
      "Test accuracy: 60.8\n",
      "\tMinibatch loss at epoch 3000: 0.9460638165473938\n",
      "\tMinibatch accuracy: 88.7\n",
      "Test accuracy: 62.9\n",
      "\tMinibatch loss at epoch 4000: 0.7436326146125793\n",
      "\tMinibatch accuracy: 90.7\n",
      "Test accuracy: 63.3\n",
      "\tMinibatch loss at epoch 5000: 0.7653072476387024\n",
      "\tMinibatch accuracy: 88.7\n",
      "Test accuracy: 62.5\n",
      "\tMinibatch loss at epoch 6000: 0.7308565974235535\n",
      "\tMinibatch accuracy: 91.8\n",
      "Test accuracy: 63.1\n",
      "\tMinibatch loss at epoch 7000: 0.7238169312477112\n",
      "\tMinibatch accuracy: 95.9\n",
      "Test accuracy: 63.5\n",
      "\tMinibatch loss at epoch 8000: 0.6555613279342651\n",
      "\tMinibatch accuracy: 95.9\n",
      "Test accuracy: 61.5\n",
      "\tMinibatch loss at epoch 9000: 0.6482402682304382\n",
      "\tMinibatch accuracy: 95.9\n",
      "Test accuracy: 64.3\n",
      "Final Test accuracy: 63.1\n",
      "time taken: 11.0 minutes 42.9 seconds\n"
     ]
    }
   ],
   "source": [
    "Run_Session(10000, 'DeepNN', .8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic CNN model made\n"
     ]
    }
   ],
   "source": [
    "# single layer CNN\n",
    "num_labels = 10\n",
    "\n",
    "batch_size = 97\n",
    "check_size = 582\n",
    "feature_size = 193\n",
    "patch_size = 10\n",
    "num_channels = 1\n",
    "depth1 = 64\n",
    "\n",
    "num_hidden = 1050\n",
    "\n",
    "beta = 0.01\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    \n",
    "    tf_data = tf.placeholder(tf.float32, shape=(None, feature_size))\n",
    "    train_labels = tf.placeholder(tf.float32, shape=(None, num_labels))\n",
    "    keep_prob = tf.placeholder(tf.float32)\n",
    "    \n",
    "    # Variables.\n",
    "    layer1_weights = weight_variable([1, patch_size, 1, depth1])\n",
    "    layer1_biases = bias_variable([depth1])\n",
    "    layer2_weights = weight_variable([(feature_size//2 + 1)* depth1, num_hidden])\n",
    "    layer2_biases = bias_variable([num_hidden])\n",
    "    layer3_weights = weight_variable([num_hidden, num_labels])\n",
    "    layer3_biases = bias_variable([num_labels])\n",
    "\n",
    "    \n",
    "    # Model with dropout\n",
    "    def model(data, distort=None, proba=keep_prob):\n",
    "        \n",
    "        #if distort is not None:\n",
    "        #    data = tf.image.random_brightness(data, 0.9, seed=58)\n",
    "        #    data = tf.image.random_contrast(data, 0.1, 1.9, seed=58)\n",
    "        \n",
    "        # Convolution\n",
    "        conv1 = tf.nn.conv2d(data, layer1_weights, [1, 1, 2, 1] , padding='SAME') + layer1_biases\n",
    "        pooled1 = tf.nn.max_pool(tf.nn.relu(conv1), ksize=[1, 1, 2, 1],strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "        # Fully Connected Layer\n",
    "        shape = pooled1.get_shape().as_list()\n",
    "        reshape = tf.reshape(pooled1, [-1, shape[1] * shape[2] * shape[3]])\n",
    "        full2 = tf.nn.relu(tf.matmul(reshape, layer2_weights) + layer2_biases)\n",
    "\n",
    "        # Dropout\n",
    "        full2 = tf.nn.dropout(full2, proba)\n",
    "        \n",
    "        return tf.matmul(full2, layer3_weights) + layer3_biases\n",
    "\n",
    "    # Training computation.\n",
    "    logits = model(tf.expand_dims(tf.expand_dims(tf_data, [-1]), 1), distort=True, proba=keep_prob)\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, train_labels) +\n",
    "                         beta*tf.nn.l2_loss(layer1_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer1_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer2_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer2_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer3_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer3_biases))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    prediction = tf.nn.softmax(logits)\n",
    "    test_prediction = tf.nn.softmax(model(tf.expand_dims(tf.expand_dims(tf_data, [-1]), 1), proba=1.0))  \n",
    "    print('Basic CNN model made')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "\tMinibatch loss at epoch 0: 4.881124496459961\n",
      "\tMinibatch accuracy: 11.3\n",
      "Test accuracy: 11.9\n",
      "\tMinibatch loss at epoch 1000: 1.1469322443008423\n",
      "\tMinibatch accuracy: 67.0\n",
      "Test accuracy: 64.3\n",
      "\tMinibatch loss at epoch 2000: 0.9210487604141235\n",
      "\tMinibatch accuracy: 84.5\n",
      "Test accuracy: 68.7\n",
      "\tMinibatch loss at epoch 3000: 0.6605882048606873\n",
      "\tMinibatch accuracy: 89.7\n",
      "Test accuracy: 70.6\n",
      "\tMinibatch loss at epoch 4000: 0.8067806959152222\n",
      "\tMinibatch accuracy: 90.7\n",
      "Test accuracy: 69.8\n",
      "\tMinibatch loss at epoch 5000: 0.6322817206382751\n",
      "\tMinibatch accuracy: 87.6\n",
      "Test accuracy: 71.8\n",
      "\tMinibatch loss at epoch 6000: 0.6652608513832092\n",
      "\tMinibatch accuracy: 91.8\n",
      "Test accuracy: 70.1\n",
      "\tMinibatch loss at epoch 7000: 0.6341663599014282\n",
      "\tMinibatch accuracy: 91.8\n",
      "Test accuracy: 68.8\n",
      "\tMinibatch loss at epoch 8000: 0.5201546549797058\n",
      "\tMinibatch accuracy: 97.9\n",
      "Test accuracy: 71.6\n",
      "\tMinibatch loss at epoch 9000: 0.6007246971130371\n",
      "\tMinibatch accuracy: 92.8\n",
      "Test accuracy: 71.0\n",
      "Final Test accuracy: 70.2\n",
      "time taken: 35.0 minutes 19.3 seconds\n"
     ]
    }
   ],
   "source": [
    "Run_Session(10000, 'CNN', .5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ezfVinTpR89fkI/TeqFQqlynBANvJs44SHJVK5RbejUbjFedeFgSBv5yIfNCj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6d4912b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,7])\n",
    "plt.title('Accuracy for 193Feature set by epoch', fontsize=20)\n",
    "plt.plot(np.array(acc_over_time['DeepNN'])[:,0], np.array(acc_over_time['DeepNN'])[:,1], label='DeepNN')\n",
    "plt.plot(np.array(acc_over_time['CNN'])[:,0], np.array(acc_over_time['CNN'])[:,1], label='CNN')\n",
    "plt.xlabel('Epoch', fontsize=18)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('Accuracy (%)', fontsize=18)\n",
    "plt.ylim([0, 100])\n",
    "plt.xlim([0, 10000])\n",
    "plt.legend(loc=\"lower right\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tSkORcH1GyyJjyf/mWfKRbriEbRk73a6xS2xH6NXXZ7O0CFa0jmgJpbz8f4pj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itnCSGEEEIIIbSTxE9HqYd66sgL3Hv0cM/pEIQQQgghhMi1ZI6fjstNad+1a1eYN292TochhBBC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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6d452da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotROC(test_labels, test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# numeralize labels\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "test_batch = le.fit_transform(test['label'])\n",
    "\n",
    "\n",
    "def confusion(true, predicted):\n",
    "    matrix = np.zeros([10,10])\n",
    "    pred = predicted.reshape((predicted.shape[0]//10, 10))\n",
    "\n",
    "    count = 0\n",
    "    for lab in test_batch:\n",
    "        matrix[lab] += pred[count].round()\n",
    "        count += 1\n",
    "        \n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zs2Zs376dpKQkihUrxvz584mPj+fq1at07tyZ0NBQXF1zf6SAKp8iIiIiDsGx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\nls8+CJ3SRAUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6d4d63198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = confusion(test_batch, test_preds['DeepNN'])\n",
    "\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.imshow(matrix, cmap='hot', interpolation='nearest',  vmin=0, vmax=200)\n",
    "plt.colorbar()\n",
    "plt.title('DeepNN Confusion Map', fontsize=18)\n",
    "plt.ylabel('Actual', fontsize=18)\n",
    "plt.xlabel('Predicted', fontsize=18)\n",
    "plt.grid(b=False)\n",
    "plt.yticks(range(10), le.classes_, fontsize=14)\n",
    "plt.xticks(range(10), le.classes_, fontsize=14, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fH4D4+HgGDBjA559/jpeXF6+//roV3x0RERGRwmnNZzFs3bqVP//8k+XLl+Pu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ejQgeXLl+v01U1NTZVNMHVs+PDhxMbGal7vXbt2MW/ePOLi4ggLC1M6nrgLcttd6NXEiRM5fvw4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6d4e07550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = confusion(test_batch, test_preds['CNN'])\n",
    "\n",
    "fig = plt.figure(figsize=[10,10])\n",
    "plt.imshow(matrix, cmap='hot', interpolation='none', vmin=0, vmax=200)\n",
    "plt.colorbar()\n",
    "plt.title('CNN Confusion Map', fontsize=18)\n",
    "plt.ylabel('Actual', fontsize=18)\n",
    "plt.xlabel('Predicted', fontsize=18)\n",
    "plt.grid(b=False)\n",
    "plt.yticks(range(10), le.classes_, fontsize=14)\n",
    "plt.xticks(range(10), le.classes_, fontsize=14, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
